Why decentralized AI is the central narrative of 2025？
I. Introduction
In the digital age, artificial intelligence (AI) is growing at an unprecedented rate, with applications extending from simple automation tasks to complex decision support systems. However, traditional cloud computing and centralized servers have encountered many challenges in handling AI requirements, including data privacy breaches, network latency, and reliance on centralized control. These challenges not only limit the development of AI technologies, but also increase operational costs and data risks.
As technology advances, more and more AI projects are introducing decentralized technology to reduce resource consumption and improve operational efficiency. Decentralized AI enhances system resilience and security by decentralizing data processing and storage, while providing faster and more reliable data processing solutions for AI applications. This shift not only optimizes the performance of AI, but also provides more autonomy and control for users and developers.
With the rise of decentralized solutions based on blockchain technology such as MEMO, the AI space is getting a boost. Decentralization will not only change the way data is processed, but will also open up new possibilities for the future of AI.
In what follows, we’ll dive into the role of blockchain technology in AI, how the DePIN network can be the biggest enabler of decentralized AI, and how MEMO can work in tandem with decentralized AI to drive innovation in this space.
Second, the role of blockchain in AI
In exploring the application of blockchain technology in AI, we first note that blockchain provides an unprecedented security protection and operation mechanism for AI. Blockchain’s distributed ledger technology ensures data immutability and transparency, which is crucial for AI systems that need to handle large amounts of sensitive data. With blockchain, AI systems are able to store and share data securely, greatly reducing the risk of data leakage and misuse.
Another core advantage of blockchain technology is its decentralized nature, which is highly compatible with the needs of AI. Decentralized AI systems are able to process data and make decisions autonomously without centralized control, and this decentralized architecture improves the resilience of the system, reduces the risk of a single point of failure, and at the same time strengthens the system’s ability to resist attacks.
Therefore, when viewed in conjunction with the basic features of blockchain technology, decentralized AI has multiple significant advantages that can facilitate broader data sharing and collaboration. With the support of blockchain, different organizations and individuals can securely share data without fear of data misuse or loss. This open data sharing environment provides rich resources for AI research and development and accelerates the pace of innovation.
Moreover, decentralized AI paves the way for the democratization of AI. By lowering the barriers to entry, more individuals and smaller organizations are able to participate in AI innovation, rather than being limited to large corporations with significant resources. This decentralized innovation model is expected to lead to more diverse solutions and advance the entire AI field.
Third, the DePIN network is either the biggest enabler of decentralized AI
Among the many decentralized innovation models, DePIN is by far the most AI-enabled sector, with significant sizeable growth in 2024, as its share of the overall crypto industry grew to 6.5% from 4.0% in 2023, and this growth momentum continues. More specifically, the number of projects focusing on AI-related research in the DePIN space reached 12.1%, a significant increase from 5.2% in 2023, indicating that the combination of DePIN and AI is becoming a new trend in the industry.
The DePIN model greatly facilitates AI resource sharing through its decentralized nature. Under this model, worldwide computing resources, storage space and network bandwidth are effectively integrated and optimally utilized, and individuals and organizations can contribute their idle computing resources to the network, thus providing the required arithmetic power for AI model training and inference.
Resource sharing not only improves the efficient use of resources, but also reduces the cost of AI programs, allowing for more innovative projects to be implemented.
And participants can get Token rewards by providing resources. The incentive mechanism encourages more individuals and organizations to join the network, thus providing the necessary support for the expansion and maintenance of the network. This revenue-based incentive model not only promotes the sharing of resources, but also provides long-term motivation for the sustainable development of the network.
IV. Synergy between MEMO and decentralized AI
The MEMO DePIN network has long been committed to powering the development of AI, and now MEMO provides a powerful system to address data and computing challenges by providing a decentralized pool of data storage and computing resources for AI systems through globally distributed nodes. These nodes not only store data, but also participate in data computation and analysis, enabling AI systems to access and process information faster and more securely.
Solutions to Data and Computing Problems:
Data storage: MEMO provides a decentralized storage system for AI data, and AI applications can store large amounts of data, including training datasets, model parameters, and user data, on the MEMO network without relying on a centralized server.
Computational power: Nodes in the MEMO network can provide additional computational power, which is especially important for AI tasks that require large amounts of computational resources. By utilizing these decentralized computational resources, AI systems can perform complex data processing and model training tasks faster.
Data Processing: the MEMO network supports processing of data where it is generated, which means that AI systems can perform data processing and analysis on the edge device closest to the data source, reducing latency and bandwidth requirements for data transmission.
In order to cover AI scenarios more comprehensively, MEMO has been fully optimized for performance and user experience:
Responsiveness: MEMO networks significantly improve the responsiveness of AI applications due to the closer proximity of data processing and storage to the data source, which is highly important for applications that require real-time feedback, such as self-driving cars and real-time surveillance systems.
Data Privacy: MEMO’s decentralized solution enhances data privacy protection using a range of protection technologies such as ZK. AI systems can process sensitive data locally without sending it to remote servers, reducing the risk of data leakage.
Cost-effectiveness: by leveraging decentralized resources on the MEMO network, AI projects can reduce their operating costs.MEMO’s incentives encourage nodes to provide compute and storage resources, which provides AI projects with more cost-effective resource options.
V. Building decentralized trust
If decentralized technology has provided a strong boost to the growth of AI, how to introduce decentralized solutions to more AI domains is what we are thinking about now. That said, building trust is critical in a decentralized network of devices, and this is a complex and critical challenge.
Ensuring mutual trust among the nodes in a network is very difficult due to the lack of a centralized authority. In a decentralized network, each device may be an independent participant, they may be under different owners and have different interests and goals. Therefore, traditional trust establishment methods, such as reputation systems or third-party authentication, may not be applicable to decentralized network environments. Instead, a new trust model can ensure that data and resources can be exchanged efficiently and securely between these devices.
Decentralized projects like MEMO are enabling interactions without trust through mathematical and computational methods. Smart contracts, for example, allow for the automatic enforcement of the terms of an agreement without an intermediary, ensuring that transactions are executed in strict accordance with predefined rules.
Data sovereignty is another core concept that allows users to have complete control over their data, deciding who can access it and how it can be used. With encryption, users can securely store and share data without relying on a centralized database.
Zero Knowledge Proof (ZK Proof) is one of the important techniques that enable the transmission of data without trust. It allows one party to prove to another party that a statement is correct without revealing any additional information. This technique ensures the authenticity and integrity of the data while protecting privacy and is important for transaction verification and authentication in decentralized networks.
In addition to MEMO, there are a large number of decentralized solutions with in-depth technology development and application in building trust, and it is the combination of these technologies that solves the challenge of trust in decentralized networks, and also provides a safer and more efficient way of interacting with the participants in the network.
VI. Future prospects
With the continuous progress of technology, decentralized AI is standing on the threshold of a new era, heralding a broad application prospect and development trend. In the next few years, we are expected to see decentralized AI achieve breakthroughs in a number of fields, such as smart cities, healthcare, and autonomous driving.
MEMO will continue to expand its network and services to support more sophisticated AI models and algorithms, optimizing the way we interact with intelligent systems and bringing far-reaching impact to a wide range of industries.
